Personalisation of Education by AI and Big Data - Lourdes Guàrdia

EADTU-EU Summit 2024

1. Personalisation of Education by AI and Big Data Prof. Lourdes Guàrdia Deputy Dean of Psychology and Education at UOC Leading the Future of Learning EADTU-EU SUMMIT 2024 Brussels, 08 May

2. 2 Opportunities Advances in AI and the availability of big data are enabling a new era of personalized education. By leveraging LA, teachers can gain deeper insights into student needs and customize instruction to support individual and group learning . Data Privacy Teacher readiness Equity and access Scalability and sustainability Bias Transparency Accountability Personalisation of Education by AI & Big Data Challenges & considerations

3. AI & LA roles and benefits for personalization Feature Artificial Intelligence (AI) Learning Analytics (LA) Focus Adapting learning experiences based on student data Providing data insights to inform personalization and individualization Role in Personalization Creates dynamic learning paths with adjusted difficulty, content type, and resources. Recommends relevant learning materials based on interests and progress. Tracks student performance data (quiz scores, activity completion…). Role in Individualization Delivers targeted instruction and feedback through intelligent tutors. Uses predictive analytics to identify potential learning difficulties. Provides automated assessment with personalized feedback. Gathers amounts of data on student behavior and progress. Identifies areas where students might be struggling. Benefits for Teachers Frees up time for individualized instruction and mentorship. Provides data for informed decision-making on curriculum adjustments. Offers insights on student needs and learning styles, performances… Benefits for Students Creates a more engaging learning experience. Ensures students are challenged at the appropriate level. Provides targeted support for individual needs. Helps students understand their strengths and weaknesses. Check how the learning progress is going on. Limitations Relies on the quality and completeness of data. May not effectively address social-emotional learning aspects. Data alone doesn't dictate personalized instruction. Requires teacher interpretation for actionable insights.

4. Leveraging LA+AI to Support Teachers AI-driven assessments can identify individual or group learning needs and gaps, enabling targeted interventions and personalized learning pathways. Personalized Assessments Competency Mapping Adaptive Learning By mapping student competencies, educators can better understand their strengths, weaknesses, and areas for growth, supporting lifelong learning. Adaptive learning technologies can dynamically adjust the content, pace, and difficulty level based on a student's performance and progress. * Images designed by the support of Copilot

5. Leveraging LA+AI to Support Teachers LA provides teachers with real-time data on student performance, engagement, and progress, empowering them to make informed, evidence-based decisions. Data-Driven Insights Personalized Interventions Continuous Improvement Using LA, teachers can identify struggling students and implement targeted strategies to address their learning needs. LA enables teachers to continuously evaluate and refine their instructional approaches, ensuring that they are meeting the evolving needs of their students. * Images designed by the support of Copilot

6. Teachers continuously evaluate and refine their instructional approaches Selecting suitable active pedagogies

7. Teachers check if activities are supporting learning objectives enough Checking e-tivities design principles

8. Follow up report-online Classroom (Canvas-PowerBI example)

9. Follow up report-online Classroom (Canvas-PowerBI)

10. Leveraging LA+AI to Support Students AI and LA can enable frequent, formative assessments that provide real-time feedback to students and teachers. Continuous Assessment Personalized Feedback Automated Grading Intelligent systems can generate tailored feedback and recommendations to help students understand their progress and areas for improvement. AI-powered grading can provide consistent, objective evaluations of student work during the learning process, specially for self-assessment, but final evaluation and validation should be done by teachers * Images designed by the support of Copilot

11. Leveraging LA+AI to Support Students AI and learning analytics can enable daily frequent data and comparative student progress. Dashboards for students ePortfolio+LA+AI Lifelong Learning AI-driven ePortfolios can provide personalized recommendations and suggestions to help students identify their strengths, address weaknesses, and set meaningful goals. ePortfolios and AI can support students in developing the essential skills and competencies needed for continuous, lifelong learning. * Images designed by the support of Copilot

12. Example: Dashboard for the student Guàrdia, L., Maina, M., Mancini, F., & Martinez Melo, M. (2023). Key Quality Factors in Digital Competence Assessment: A Validation Study from Teachers’ Perspective. Applied Sciences, 13 (4), 2450. https://doi.org/10.3390/app13042450

13. Example: Student Competency Map

14. Example: Personal ePortfolio

15. UOC.universitat @UOCuniversitat @uocuniversitat THANKS Lourdes Guàrdia [email protected] @louguardia